The Silent Signal: Unmasking Myocardial Ischemia in a Resting Heartbeat with Machine Learning

BackgroundIschemic heart disease (IHD) remains the leading cause of morbidity and mortality worldwide, imposing a staggering burden on healthcare systems and societies. AimsTo assess the diagnostic capabilities of the single lead electrocardiography in the diagnosis of IHD using the machine learning model. Materials and methodsA prospective, non-randomized, minimally invasive, single-center, case-control study enrolled male and female participants aged [&ge;]40 years. All participants underwent resting single-lead electrocardiography (SLECG) and pulse wave recording using a portable Cardio-Qvark(R) device, and stress computed tomography myocardial perfusion imaging with vasodilation test. Based on coronary computed tomography perfusion (CTP) results, 80 participants were stratified into two groups: Group 1 with stress-induced myocardial perfusion defect (n = 31) and Group 2 without stress-induced myocardial perfusion defect (n = 49). Statistical processing carried out using the R programming language v4.2, Python V.3, Statistica 12 programme. (StatSoft, Inc. (2014). STATISTICA (data analysis software system), version 12. www.statsoft.com.), and SPSS IBM version 28. P considered statistically significant at <0.05. ResultsThe best model performance in the diagnosis of IHD was Random Forest model. The model showed a diagnostic accuracy based on the parameters of the SLECG, AUC 0.988 [95 % confidence interval (CI); 0.967-1.000], Sensitivity 0.871 [95 % CI; 0.739-0.971], Specificity 0.959 [ 95 % CI; 0.894-1.000]. ConclusionThis study demonstrates that machine learning analysis of resting single-lead ECG signals, acquired via the portable Cardio-Qvark(R) device, achieves near-perfect accuracy (AUC 0.988) in diagnosing ischemic heart disease validated against myocardial perfusion imaging.